Multi-View Learning. In 3D computer vision, recent works have focused on learning better representation by rendering objects from different views.
MV3D [1]} encodes the sparse 3D point cloud with a compact multi-view (e.g., bird's eye view and front view) representation for the 3D detection task [2]}.
MVCNN [3]} completes the 3d object classification by rendering a large number of 2d pictures from 3d objects. These works project 3D point clouds onto different 2D planes to enhance visual features. Unlike them, we project the positional information of the 3D scene into a multi-view space, thereby learning a view-robust representation for better vision-language alignment.
